{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xieyipeng/Documents/MachineLearning/wuenda_py/course2/do_not_src/week2_2/opt_utils.py:76: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n",
      "  assert(parameters['W' + str(l)].shape == layer_dims[l], layer_dims[l-1])\n",
      "/home/xieyipeng/Documents/MachineLearning/wuenda_py/course2/do_not_src/week2_2/opt_utils.py:77: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n",
      "  assert(parameters['W' + str(l)].shape == layer_dims[l], 1)\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "import math\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "\n",
    "from do_not_src.week2_2 import opt_utils #参见数据包或者在本文底部copy\n",
    "from do_not_src.week2_2 import testCase  #参见数据包或者在本文底部copy\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_parameters_with_gd(parameters,grads,learning_rate):\n",
    "    \"\"\"\n",
    "    使用梯度下降更新参数\n",
    "    \n",
    "    参数：\n",
    "        parameters - 字典，包含了要更新的参数：\n",
    "            parameters['W' + str(l)] = Wl\n",
    "            parameters['b' + str(l)] = bl\n",
    "        grads - 字典，包含了每一个梯度值用以更新参数\n",
    "            grads['dW' + str(l)] = dWl\n",
    "            grads['db' + str(l)] = dbl\n",
    "        learning_rate - 学习率\n",
    "        \n",
    "    返回值：\n",
    "        parameters - 字典，包含了更新后的参数\n",
    "    \"\"\"\n",
    "    \n",
    "    L = len(parameters) // 2 #神经网络的层数\n",
    "    \n",
    "    #更新每个参数\n",
    "    for l in range(L):\n",
    "        parameters[\"W\" + str(l +1)] = parameters[\"W\" + str(l + 1)] - learning_rate * grads[\"dW\" + str(l + 1)]\n",
    "        parameters[\"b\" + str(l +1)] = parameters[\"b\" + str(l + 1)] - learning_rate * grads[\"db\" + str(l + 1)]\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------测试update_parameters_with_gd-------------\n",
      "W1 = [[ 1.63535156 -0.62320365 -0.53718766]\n",
      " [-1.07799357  0.85639907 -2.29470142]]\n",
      "b1 = [[ 1.74604067]\n",
      " [-0.75184921]]\n",
      "W2 = [[ 0.32171798 -0.25467393  1.46902454]\n",
      " [-2.05617317 -0.31554548 -0.3756023 ]\n",
      " [ 1.1404819  -1.09976462 -0.1612551 ]]\n",
      "b2 = [[-0.88020257]\n",
      " [ 0.02561572]\n",
      " [ 0.57539477]]\n"
     ]
    }
   ],
   "source": [
    "#测试update_parameters_with_gd\n",
    "print(\"-------------测试update_parameters_with_gd-------------\")\n",
    "parameters , grads , learning_rate = testCase.update_parameters_with_gd_test_case()\n",
    "parameters = update_parameters_with_gd(parameters,grads,learning_rate)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_mini_batches(X,Y,mini_batch_size=64,seed=0):\n",
    "    \"\"\"\n",
    "    从（X，Y）中创建一个随机的mini-batch列表\n",
    "    \n",
    "    参数：\n",
    "        X - 输入数据，维度为(输入节点数量，样本的数量)\n",
    "        Y - 对应的是X的标签，【1 | 0】（蓝|红），维度为(1,样本的数量)\n",
    "        mini_batch_size - 每个mini-batch的样本数量\n",
    "        \n",
    "    返回：\n",
    "        mini-bacthes - 一个同步列表，维度为（mini_batch_X,mini_batch_Y）\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(seed) #指定随机种子\n",
    "    m = X.shape[1]\n",
    "    mini_batches = []\n",
    "    \n",
    "    #第一步：打乱顺序\n",
    "    permutation = list(np.random.permutation(m)) #它会返回一个长度为m的随机数组，且里面的数是0到m-1\n",
    "    shuffled_X = X[:,permutation]   #将每一列的数据按permutation的顺序来重新排列。\n",
    "    shuffled_Y = Y[:,permutation].reshape((1,m))\n",
    "    \n",
    "    \"\"\"\n",
    "    #博主注：\n",
    "    #如果你不好理解的话请看一下下面的伪代码，看看X和Y是如何根据permutation来打乱顺序的。\n",
    "    x = np.array([[1,2,3,4,5,6,7,8,9],\n",
    "\t\t\t\t  [9,8,7,6,5,4,3,2,1]])\n",
    "    y = np.array([[1,0,1,0,1,0,1,0,1]])\n",
    "    \n",
    "    random_mini_batches(x,y)\n",
    "    permutation= [7, 2, 1, 4, 8, 6, 3, 0, 5]\n",
    "    shuffled_X= [[8 3 2 5 9 7 4 1 6]\n",
    "                 [2 7 8 5 1 3 6 9 4]]\n",
    "    shuffled_Y= [[0 1 0 1 1 1 0 1 0]]\n",
    "    \"\"\"\n",
    "    \n",
    "    #第二步，分割\n",
    "    num_complete_minibatches = math.floor(m / mini_batch_size) #把你的训练集分割成多少份,请注意，如果值是99.99，那么返回值是99，剩下的0.99会被舍弃\n",
    "    for k in range(0,num_complete_minibatches):\n",
    "        mini_batch_X = shuffled_X[:,k * mini_batch_size:(k+1)*mini_batch_size]\n",
    "        mini_batch_Y = shuffled_Y[:,k * mini_batch_size:(k+1)*mini_batch_size]\n",
    "        \"\"\"\n",
    "        #博主注：\n",
    "        #如果你不好理解的话请单独执行下面的代码，它可以帮你理解一些。\n",
    "        a = np.array([[1,2,3,4,5,6,7,8,9],\n",
    "                      [9,8,7,6,5,4,3,2,1],\n",
    "                      [1,2,3,4,5,6,7,8,9]])\n",
    "        k=1\n",
    "        mini_batch_size=3\n",
    "        print(a[:,1*3:(1+1)*3]) #从第4列到第6列\n",
    "        '''\n",
    "        [[4 5 6]\n",
    "         [6 5 4]\n",
    "         [4 5 6]]\n",
    "        '''\n",
    "        k=2\n",
    "        print(a[:,2*3:(2+1)*3]) #从第7列到第9列\n",
    "        '''\n",
    "        [[7 8 9]\n",
    "         [3 2 1]\n",
    "         [7 8 9]]\n",
    "        '''\n",
    "\n",
    "        #看一下每一列的数据你可能就会好理解一些\n",
    "        \"\"\"\n",
    "        mini_batch = (mini_batch_X,mini_batch_Y)\n",
    "        mini_batches.append(mini_batch)\n",
    "    \n",
    "    #如果训练集的大小刚好是mini_batch_size的整数倍，那么这里已经处理完了\n",
    "    #如果训练集的大小不是mini_batch_size的整数倍，那么最后肯定会剩下一些，我们要把它处理了\n",
    "    if m % mini_batch_size != 0:\n",
    "        #获取最后剩余的部分\n",
    "        mini_batch_X = shuffled_X[:,mini_batch_size * num_complete_minibatches:]\n",
    "        mini_batch_Y = shuffled_Y[:,mini_batch_size * num_complete_minibatches:]\n",
    "        \n",
    "        mini_batch = (mini_batch_X,mini_batch_Y)\n",
    "        mini_batches.append(mini_batch)\n",
    "        \n",
    "    return mini_batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------测试random_mini_batches-------------\n",
      "第1个mini_batch_X 的维度为： (12288, 64)\n",
      "第1个mini_batch_Y 的维度为： (1, 64)\n",
      "第2个mini_batch_X 的维度为： (12288, 64)\n",
      "第2个mini_batch_Y 的维度为： (1, 64)\n",
      "第3个mini_batch_X 的维度为： (12288, 20)\n",
      "第3个mini_batch_Y 的维度为： (1, 20)\n"
     ]
    }
   ],
   "source": [
    "#测试random_mini_batches\n",
    "print(\"-------------测试random_mini_batches-------------\")\n",
    "X_assess,Y_assess,mini_batch_size = testCase.random_mini_batches_test_case()\n",
    "mini_batches = random_mini_batches(X_assess,Y_assess,mini_batch_size)\n",
    "\n",
    "print(\"第1个mini_batch_X 的维度为：\",mini_batches[0][0].shape)\n",
    "print(\"第1个mini_batch_Y 的维度为：\",mini_batches[0][1].shape)\n",
    "print(\"第2个mini_batch_X 的维度为：\",mini_batches[1][0].shape)\n",
    "print(\"第2个mini_batch_Y 的维度为：\",mini_batches[1][1].shape)\n",
    "print(\"第3个mini_batch_X 的维度为：\",mini_batches[2][0].shape)\n",
    "print(\"第3个mini_batch_Y 的维度为：\",mini_batches[2][1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_velocity(parameters):\n",
    "    \"\"\"\n",
    "    初始化速度，velocity是一个字典：\n",
    "        - keys: \"dW1\", \"db1\", ..., \"dWL\", \"dbL\" \n",
    "        - values:与相应的梯度/参数维度相同的值为零的矩阵。\n",
    "    参数：\n",
    "        parameters - 一个字典，包含了以下参数：\n",
    "            parameters[\"W\" + str(l)] = Wl\n",
    "            parameters[\"b\" + str(l)] = bl\n",
    "    返回:\n",
    "        v - 一个字典变量，包含了以下参数：\n",
    "            v[\"dW\" + str(l)] = dWl的速度\n",
    "            v[\"db\" + str(l)] = dbl的速度\n",
    "    \n",
    "    \"\"\"\n",
    "    L = len(parameters) // 2 #神经网络的层数\n",
    "    v = {}\n",
    "    \n",
    "    for l in range(L):\n",
    "        v[\"dW\" + str(l + 1)] = np.zeros_like(parameters[\"W\" + str(l + 1)])\n",
    "        v[\"db\" + str(l + 1)] = np.zeros_like(parameters[\"b\" + str(l + 1)])\n",
    "    \n",
    "    return v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------测试initialize_velocity-------------\n",
      "v[\"dW1\"] = [[0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "v[\"db1\"] = [[0.]\n",
      " [0.]]\n",
      "v[\"dW2\"] = [[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "v[\"db2\"] = [[0.]\n",
      " [0.]\n",
      " [0.]]\n"
     ]
    }
   ],
   "source": [
    "#测试initialize_velocity\n",
    "print(\"-------------测试initialize_velocity-------------\")\n",
    "parameters = testCase.initialize_velocity_test_case()\n",
    "v = initialize_velocity(parameters)\n",
    "\n",
    "print('v[\"dW1\"] = ' + str(v[\"dW1\"]))\n",
    "print('v[\"db1\"] = ' + str(v[\"db1\"]))\n",
    "print('v[\"dW2\"] = ' + str(v[\"dW2\"]))\n",
    "print('v[\"db2\"] = ' + str(v[\"db2\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_parameters_with_momentun(parameters,grads,v,beta,learning_rate):\n",
    "    \"\"\"\n",
    "    使用动量更新参数\n",
    "    参数：\n",
    "        parameters - 一个字典类型的变量，包含了以下字段：\n",
    "            parameters[\"W\" + str(l)] = Wl\n",
    "            parameters[\"b\" + str(l)] = bl\n",
    "        grads - 一个包含梯度值的字典变量，具有以下字段：\n",
    "            grads[\"dW\" + str(l)] = dWl\n",
    "            grads[\"db\" + str(l)] = dbl\n",
    "        v - 包含当前速度的字典变量，具有以下字段：\n",
    "            v[\"dW\" + str(l)] = ...\n",
    "            v[\"db\" + str(l)] = ...\n",
    "        beta - 超参数，动量，实数\n",
    "        learning_rate - 学习率，实数\n",
    "    返回：\n",
    "        parameters - 更新后的参数字典\n",
    "        v - 包含了更新后的速度变量\n",
    "    \"\"\"\n",
    "    L = len(parameters) // 2 \n",
    "    for l in range(L):\n",
    "        #计算速度\n",
    "        v[\"dW\" + str(l + 1)] = beta * v[\"dW\" + str(l + 1)] + (1 - beta) * grads[\"dW\" + str(l + 1)]\n",
    "        v[\"db\" + str(l + 1)] = beta * v[\"db\" + str(l + 1)] + (1 - beta) * grads[\"db\" + str(l + 1)]\n",
    "        \n",
    "        #更新参数\n",
    "        parameters[\"W\" + str(l + 1)] = parameters[\"W\" + str(l + 1)] - learning_rate * v[\"dW\" + str(l + 1)]\n",
    "        parameters[\"b\" + str(l + 1)] = parameters[\"b\" + str(l + 1)] - learning_rate * v[\"db\" + str(l + 1)]\n",
    "    \n",
    "    return parameters,v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------测试update_parameters_with_momentun-------------\n",
      "W1 = [[ 1.62544598 -0.61290114 -0.52907334]\n",
      " [-1.07347112  0.86450677 -2.30085497]]\n",
      "b1 = [[ 1.74493465]\n",
      " [-0.76027113]]\n",
      "W2 = [[ 0.31930698 -0.24990073  1.4627996 ]\n",
      " [-2.05974396 -0.32173003 -0.38320915]\n",
      " [ 1.13444069 -1.0998786  -0.1713109 ]]\n",
      "b2 = [[-0.87809283]\n",
      " [ 0.04055394]\n",
      " [ 0.58207317]]\n",
      "v[\"dW1\"] = [[-0.11006192  0.11447237  0.09015907]\n",
      " [ 0.05024943  0.09008559 -0.06837279]]\n",
      "v[\"db1\"] = [[-0.01228902]\n",
      " [-0.09357694]]\n",
      "v[\"dW2\"] = [[-0.02678881  0.05303555 -0.06916608]\n",
      " [-0.03967535 -0.06871727 -0.08452056]\n",
      " [-0.06712461 -0.00126646 -0.11173103]]\n",
      "v[\"db2\"] = [[0.02344157]\n",
      " [0.16598022]\n",
      " [0.07420442]]\n"
     ]
    }
   ],
   "source": [
    "#测试update_parameters_with_momentun\n",
    "print(\"-------------测试update_parameters_with_momentun-------------\")\n",
    "parameters,grads,v = testCase.update_parameters_with_momentum_test_case()\n",
    "update_parameters_with_momentun(parameters,grads,v,beta=0.9,learning_rate=0.01)\n",
    "\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))\n",
    "print('v[\"dW1\"] = ' + str(v[\"dW1\"]))\n",
    "print('v[\"db1\"] = ' + str(v[\"db1\"]))\n",
    "print('v[\"dW2\"] = ' + str(v[\"dW2\"]))\n",
    "print('v[\"db2\"] = ' + str(v[\"db2\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_adam(parameters):\n",
    "    \"\"\"\n",
    "    初始化v和s，它们都是字典类型的变量，都包含了以下字段：\n",
    "        - keys: \"dW1\", \"db1\", ..., \"dWL\", \"dbL\" \n",
    "        - values：与对应的梯度/参数相同维度的值为零的numpy矩阵\n",
    "    \n",
    "    参数：\n",
    "        parameters - 包含了以下参数的字典变量：\n",
    "            parameters[\"W\" + str(l)] = Wl\n",
    "            parameters[\"b\" + str(l)] = bl\n",
    "    返回：\n",
    "        v - 包含梯度的指数加权平均值，字段如下：\n",
    "            v[\"dW\" + str(l)] = ...\n",
    "            v[\"db\" + str(l)] = ...\n",
    "        s - 包含平方梯度的指数加权平均值，字段如下：\n",
    "            s[\"dW\" + str(l)] = ...\n",
    "            s[\"db\" + str(l)] = ...\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    L = len(parameters) // 2\n",
    "    v = {}\n",
    "    s = {}\n",
    "    \n",
    "    for l in range(L):\n",
    "        v[\"dW\" + str(l + 1)] = np.zeros_like(parameters[\"W\" + str(l + 1)])\n",
    "        v[\"db\" + str(l + 1)] = np.zeros_like(parameters[\"b\" + str(l + 1)])\n",
    "        \n",
    "        s[\"dW\" + str(l + 1)] = np.zeros_like(parameters[\"W\" + str(l + 1)])\n",
    "        s[\"db\" + str(l + 1)] = np.zeros_like(parameters[\"b\" + str(l + 1)])\n",
    "    \n",
    "    return (v,s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------测试initialize_adam-------------\n",
      "v[\"dW1\"] = [[0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "v[\"db1\"] = [[0.]\n",
      " [0.]]\n",
      "v[\"dW2\"] = [[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "v[\"db2\"] = [[0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "s[\"dW1\"] = [[0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "s[\"db1\"] = [[0.]\n",
      " [0.]]\n",
      "s[\"dW2\"] = [[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "s[\"db2\"] = [[0.]\n",
      " [0.]\n",
      " [0.]]\n"
     ]
    }
   ],
   "source": [
    "#测试initialize_adam\n",
    "print(\"-------------测试initialize_adam-------------\")\n",
    "parameters = testCase.initialize_adam_test_case()\n",
    "v,s = initialize_adam(parameters)\n",
    "\n",
    "print('v[\"dW1\"] = ' + str(v[\"dW1\"])) \n",
    "print('v[\"db1\"] = ' + str(v[\"db1\"])) \n",
    "print('v[\"dW2\"] = ' + str(v[\"dW2\"])) \n",
    "print('v[\"db2\"] = ' + str(v[\"db2\"])) \n",
    "print('s[\"dW1\"] = ' + str(s[\"dW1\"])) \n",
    "print('s[\"db1\"] = ' + str(s[\"db1\"])) \n",
    "print('s[\"dW2\"] = ' + str(s[\"dW2\"])) \n",
    "print('s[\"db2\"] = ' + str(s[\"db2\"])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_parameters_with_adam(parameters,grads,v,s,t,learning_rate=0.01,beta1=0.9,beta2=0.999,epsilon=1e-8):\n",
    "    \"\"\"\n",
    "    使用Adam更新参数\n",
    "    \n",
    "    参数：\n",
    "        parameters - 包含了以下字段的字典：\n",
    "            parameters['W' + str(l)] = Wl\n",
    "            parameters['b' + str(l)] = bl\n",
    "        grads - 包含了梯度值的字典，有以下key值：\n",
    "            grads['dW' + str(l)] = dWl\n",
    "            grads['db' + str(l)] = dbl\n",
    "        v - Adam的变量，第一个梯度的移动平均值，是一个字典类型的变量\n",
    "        s - Adam的变量，平方梯度的移动平均值，是一个字典类型的变量\n",
    "        t - 当前迭代的次数\n",
    "        learning_rate - 学习率\n",
    "        beta1 - 动量，超参数,用于第一阶段，使得曲线的Y值不从0开始（参见天气数据的那个图）\n",
    "        beta2 - RMSprop的一个参数，超参数\n",
    "        epsilon - 防止除零操作（分母为0）\n",
    "    \n",
    "    返回：\n",
    "        parameters - 更新后的参数\n",
    "        v - 第一个梯度的移动平均值，是一个字典类型的变量\n",
    "        s - 平方梯度的移动平均值，是一个字典类型的变量\n",
    "    \"\"\"\n",
    "    L = len(parameters) // 2\n",
    "    v_corrected = {} #偏差修正后的值\n",
    "    s_corrected = {} #偏差修正后的值\n",
    "    \n",
    "    for l in range(L):\n",
    "        #梯度的移动平均值,输入：\"v , grads , beta1\",输出：\" v \"\n",
    "        v[\"dW\" + str(l + 1)] = beta1 * v[\"dW\" + str(l + 1)] + (1 - beta1) * grads[\"dW\" + str(l + 1)]\n",
    "        v[\"db\" + str(l + 1)] = beta1 * v[\"db\" + str(l + 1)] + (1 - beta1) * grads[\"db\" + str(l + 1)]\n",
    "        \n",
    "        #计算第一阶段的偏差修正后的估计值，输入\"v , beta1 , t\" , 输出：\"v_corrected\"\n",
    "        v_corrected[\"dW\" + str(l + 1)] = v[\"dW\" + str(l + 1)] / (1 - np.power(beta1,t))\n",
    "        v_corrected[\"db\" + str(l + 1)] = v[\"db\" + str(l + 1)] / (1 - np.power(beta1,t))\n",
    "    \n",
    "        #计算平方梯度的移动平均值，输入：\"s, grads , beta2\"，输出：\"s\"\n",
    "        s[\"dW\" + str(l + 1)] = beta2 * s[\"dW\" + str(l + 1)] + (1 - beta2) * np.square(grads[\"dW\" + str(l + 1)])\n",
    "        s[\"db\" + str(l + 1)] = beta2 * s[\"db\" + str(l + 1)] + (1 - beta2) * np.square(grads[\"db\" + str(l + 1)])\n",
    "         \n",
    "        #计算第二阶段的偏差修正后的估计值，输入：\"s , beta2 , t\"，输出：\"s_corrected\"\n",
    "        s_corrected[\"dW\" + str(l + 1)] = s[\"dW\" + str(l + 1)] / (1 - np.power(beta2,t))\n",
    "        s_corrected[\"db\" + str(l + 1)] = s[\"db\" + str(l + 1)] / (1 - np.power(beta2,t))\n",
    "        \n",
    "        #更新参数，输入: \"parameters, learning_rate, v_corrected, s_corrected, epsilon\". 输出: \"parameters\".\n",
    "        parameters[\"W\" + str(l + 1)] = parameters[\"W\" + str(l + 1)] - learning_rate * (v_corrected[\"dW\" + str(l + 1)] / np.sqrt(s_corrected[\"dW\" + str(l + 1)] + epsilon))\n",
    "        parameters[\"b\" + str(l + 1)] = parameters[\"b\" + str(l + 1)] - learning_rate * (v_corrected[\"db\" + str(l + 1)] / np.sqrt(s_corrected[\"db\" + str(l + 1)] + epsilon))\n",
    "    \n",
    "    return (parameters,v,s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------测试update_with_parameters_with_adam-------------\n",
      "W1 = [[ 1.63178673 -0.61919778 -0.53561312]\n",
      " [-1.08040999  0.85796626 -2.29409733]]\n",
      "b1 = [[ 1.75225313]\n",
      " [-0.75376553]]\n",
      "W2 = [[ 0.32648046 -0.25681174  1.46954931]\n",
      " [-2.05269934 -0.31497584 -0.37661299]\n",
      " [ 1.14121081 -1.09245036 -0.16498684]]\n",
      "b2 = [[-0.88529978]\n",
      " [ 0.03477238]\n",
      " [ 0.57537385]]\n",
      "v[\"dW1\"] = [[-0.11006192  0.11447237  0.09015907]\n",
      " [ 0.05024943  0.09008559 -0.06837279]]\n",
      "v[\"db1\"] = [[-0.01228902]\n",
      " [-0.09357694]]\n",
      "v[\"dW2\"] = [[-0.02678881  0.05303555 -0.06916608]\n",
      " [-0.03967535 -0.06871727 -0.08452056]\n",
      " [-0.06712461 -0.00126646 -0.11173103]]\n",
      "v[\"db2\"] = [[0.02344157]\n",
      " [0.16598022]\n",
      " [0.07420442]]\n",
      "s[\"dW1\"] = [[0.00121136 0.00131039 0.00081287]\n",
      " [0.0002525  0.00081154 0.00046748]]\n",
      "s[\"db1\"] = [[1.51020075e-05]\n",
      " [8.75664434e-04]]\n",
      "s[\"dW2\"] = [[7.17640232e-05 2.81276921e-04 4.78394595e-04]\n",
      " [1.57413361e-04 4.72206320e-04 7.14372576e-04]\n",
      " [4.50571368e-04 1.60392066e-07 1.24838242e-03]]\n",
      "s[\"db2\"] = [[5.49507194e-05]\n",
      " [2.75494327e-03]\n",
      " [5.50629536e-04]]\n"
     ]
    }
   ],
   "source": [
    "#测试update_with_parameters_with_adam\n",
    "print(\"-------------测试update_with_parameters_with_adam-------------\")\n",
    "parameters , grads , v , s = testCase.update_parameters_with_adam_test_case()\n",
    "update_parameters_with_adam(parameters,grads,v,s,t=2)\n",
    "\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))\n",
    "print('v[\"dW1\"] = ' + str(v[\"dW1\"])) \n",
    "print('v[\"db1\"] = ' + str(v[\"db1\"])) \n",
    "print('v[\"dW2\"] = ' + str(v[\"dW2\"])) \n",
    "print('v[\"db2\"] = ' + str(v[\"db2\"])) \n",
    "print('s[\"dW1\"] = ' + str(s[\"dW1\"])) \n",
    "print('s[\"db1\"] = ' + str(s[\"db1\"])) \n",
    "print('s[\"dW2\"] = ' + str(s[\"dW2\"])) \n",
    "print('s[\"db2\"] = ' + str(s[\"db2\"])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_X, train_Y = opt_utils.load_dataset(is_plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X,Y,layers_dims,optimizer,learning_rate=0.0007,\n",
    "          mini_batch_size=64,beta=0.9,beta1=0.9,beta2=0.999,\n",
    "          epsilon=1e-8,num_epochs=10000,print_cost=True,is_plot=True):\n",
    "    \n",
    "    \"\"\"\n",
    "    可以运行在不同优化器模式下的3层神经网络模型。\n",
    "    \n",
    "    参数：\n",
    "        X - 输入数据，维度为（2，输入的数据集里面样本数量）\n",
    "        Y - 与X对应的标签\n",
    "        layers_dims - 包含层数和节点数量的列表\n",
    "        optimizer - 字符串类型的参数，用于选择优化类型，【 \"gd\" | \"momentum\" | \"adam\" 】\n",
    "        learning_rate - 学习率\n",
    "        mini_batch_size - 每个小批量数据集的大小\n",
    "        beta - 用于动量优化的一个超参数\n",
    "        beta1 - 用于计算梯度后的指数衰减的估计的超参数\n",
    "        beta1 - 用于计算平方梯度后的指数衰减的估计的超参数\n",
    "        epsilon - 用于在Adam中避免除零操作的超参数，一般不更改\n",
    "        num_epochs - 整个训练集的遍历次数，（视频2.9学习率衰减，1分55秒处，视频中称作“代”）,相当于之前的num_iteration\n",
    "        print_cost - 是否打印误差值，每遍历1000次数据集打印一次，但是每100次记录一个误差值，又称每1000代打印一次\n",
    "        is_plot - 是否绘制出曲线图\n",
    "        \n",
    "    返回：\n",
    "        parameters - 包含了学习后的参数\n",
    "        \n",
    "    \"\"\"\n",
    "    L = len(layers_dims)\n",
    "    costs = []\n",
    "    t = 0 #每学习完一个minibatch就增加1\n",
    "    seed = 10 #随机种子\n",
    "    \n",
    "    #初始化参数\n",
    "    parameters = opt_utils.initialize_parameters(layers_dims)\n",
    "    \n",
    "    #选择优化器\n",
    "    if optimizer == \"gd\":\n",
    "        pass #不使用任何优化器，直接使用梯度下降法\n",
    "    elif optimizer == \"momentum\":\n",
    "        v = initialize_velocity(parameters) #使用动量\n",
    "    elif optimizer == \"adam\":\n",
    "        v, s = initialize_adam(parameters)#使用Adam优化\n",
    "    else:\n",
    "        print(\"optimizer参数错误，程序退出。\")\n",
    "        exit(1)\n",
    "    \n",
    "    #开始学习\n",
    "    for i in range(num_epochs):\n",
    "        #定义随机 minibatches,我们在每次遍历数据集之后增加种子以重新排列数据集，使每次数据的顺序都不同\n",
    "        seed = seed + 1\n",
    "        minibatches = random_mini_batches(X,Y,mini_batch_size,seed)\n",
    "        \n",
    "        for minibatch in minibatches:\n",
    "            #选择一个minibatch\n",
    "            (minibatch_X,minibatch_Y) = minibatch\n",
    "            \n",
    "            #前向传播\n",
    "            A3 , cache = opt_utils.forward_propagation(minibatch_X,parameters)\n",
    "            \n",
    "            #计算误差\n",
    "            cost = opt_utils.compute_cost(A3 , minibatch_Y)\n",
    "            \n",
    "            #反向传播\n",
    "            grads = opt_utils.backward_propagation(minibatch_X,minibatch_Y,cache)\n",
    "            \n",
    "            #更新参数\n",
    "            if optimizer == \"gd\":\n",
    "                parameters = update_parameters_with_gd(parameters,grads,learning_rate)\n",
    "            elif optimizer == \"momentum\":\n",
    "                parameters, v = update_parameters_with_momentun(parameters,grads,v,beta,learning_rate)\n",
    "            elif optimizer == \"adam\":\n",
    "                t = t + 1 \n",
    "                parameters , v , s = update_parameters_with_adam(parameters,grads,v,s,t,learning_rate,beta1,beta2,epsilon)\n",
    "        #记录误差值\n",
    "        if i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "            #是否打印误差值\n",
    "            if print_cost and i % 1000 == 0:\n",
    "                print(\"第\" + str(i) + \"次遍历整个数据集，当前误差值：\" + str(cost))\n",
    "    #是否绘制曲线图\n",
    "    if is_plot:\n",
    "        plt.plot(costs)\n",
    "        plt.ylabel('cost')\n",
    "        plt.xlabel('epochs (per 100)')\n",
    "        plt.title(\"Learning rate = \" + str(learning_rate))\n",
    "        plt.show()\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第0次遍历整个数据集，当前误差值：0.690735512291113\n",
      "第1000次遍历整个数据集，当前误差值：0.6852725328458241\n",
      "第2000次遍历整个数据集，当前误差值：0.6470722240719003\n",
      "第3000次遍历整个数据集，当前误差值：0.6195245549970403\n",
      "第4000次遍历整个数据集，当前误差值：0.5765844355950944\n",
      "第5000次遍历整个数据集，当前误差值：0.6072426395968576\n",
      "第6000次遍历整个数据集，当前误差值：0.5294033317684576\n",
      "第7000次遍历整个数据集，当前误差值：0.46076823985930115\n",
      "第8000次遍历整个数据集，当前误差值：0.465586082399045\n",
      "第9000次遍历整个数据集，当前误差值：0.46451797221676844\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用普通的梯度下降\n",
    "layers_dims = [train_X.shape[0],5,2,1]\n",
    "parameters = model(train_X, train_Y, layers_dims, optimizer=\"gd\",is_plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7966666666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#预测\n",
    "preditions = opt_utils.predict(train_X,train_Y,parameters)\n",
    "\n",
    "#绘制分类图\n",
    "plt.title(\"Model with Gradient Descent optimization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5, 2.5])\n",
    "axes.set_ylim([-1, 1.5])\n",
    "opt_utils.plot_decision_boundary(lambda x: opt_utils.predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第0次遍历整个数据集，当前误差值：0.6907412988351506\n",
      "第1000次遍历整个数据集，当前误差值：0.6853405261267578\n",
      "第2000次遍历整个数据集，当前误差值：0.6471448370095255\n",
      "第3000次遍历整个数据集，当前误差值：0.6195943032076022\n",
      "第4000次遍历整个数据集，当前误差值：0.5766650344073023\n",
      "第5000次遍历整个数据集，当前误差值：0.607323821900647\n",
      "第6000次遍历整个数据集，当前误差值：0.5294761758786997\n",
      "第7000次遍历整个数据集，当前误差值：0.46093619004872366\n",
      "第8000次遍历整个数据集，当前误差值：0.465780093701272\n",
      "第9000次遍历整个数据集，当前误差值：0.4647395967922748\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "layers_dims = [train_X.shape[0],5,2,1]\n",
    "#使用动量的梯度下降\n",
    "parameters = model(train_X, train_Y, layers_dims, beta=0.9,optimizer=\"momentum\",is_plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7966666666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#预测\n",
    "preditions = opt_utils.predict(train_X,train_Y,parameters)\n",
    "\n",
    "#绘制分类图\n",
    "plt.title(\"Model with Momentum optimization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5, 2.5])\n",
    "axes.set_ylim([-1, 1.5])\n",
    "opt_utils.plot_decision_boundary(lambda x: opt_utils.predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第0次遍历整个数据集，当前误差值：0.6905522446113365\n",
      "第1000次遍历整个数据集，当前误差值：0.18550136438550574\n",
      "第2000次遍历整个数据集，当前误差值：0.15083046575253203\n",
      "第3000次遍历整个数据集，当前误差值：0.0744543857099718\n",
      "第4000次遍历整个数据集，当前误差值：0.1259591565133716\n",
      "第5000次遍历整个数据集，当前误差值：0.10434443534245479\n",
      "第6000次遍历整个数据集，当前误差值：0.10067637504120665\n",
      "第7000次遍历整个数据集，当前误差值：0.031652030135115576\n",
      "第8000次遍历整个数据集，当前误差值：0.111972731312442\n",
      "第9000次遍历整个数据集，当前误差值：0.19794007152465484\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "layers_dims = [train_X.shape[0], 5, 2, 1]\n",
    "#使用Adam优化的梯度下降\n",
    "parameters = model(train_X, train_Y, layers_dims, optimizer=\"adam\",is_plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.94\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#预测\n",
    "preditions = opt_utils.predict(train_X,train_Y,parameters)\n",
    "\n",
    "#绘制分类图\n",
    "plt.title(\"Model with Adam optimization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5, 2.5])\n",
    "axes.set_ylim([-1, 1.5])\n",
    "opt_utils.plot_decision_boundary(lambda x: opt_utils.predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
